Normalization seismic attribute

ABSTRACT

A method can include providing seismic data values for a subsurface region that includes a reflector; determining a gradient magnitude value based on at least a portion of the seismic data values; normalizing the gradient magnitude value using a nonlinear normalization equation that includes a gradient magnitude variable divided by a normalization variable raised to a power that depends on an adjustable parameter; and outputting the normalized gradient magnitude value. Various other apparatuses, systems, methods, etc., are also disclosed.

RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication having Ser. No. 61/746,491, filed 27 Dec. 2012, which isincorporated by reference herein.

BACKGROUND

Reflection seismology finds use in geophysics, for example, to estimateproperties of subsurface formations. As an example, reflectionseismology may provide seismic data representing waves of elastic energy(e.g., as transmitted by P-waves and S-waves, in a frequency range ofapproximately 1 Hz to approximately 100 Hz). Seismic data may beprocessed and interpreted, for example, to understand bettercomposition, fluid content, extent and geometry of subsurface rocks.Various techniques described herein pertain to processing of data suchas, for example, seismic data.

SUMMARY

A method can include providing data values for a region; determining agradient magnitude value based on at least a portion of the data values;normalizing the gradient magnitude value using a nonlinear normalizationequation that includes a gradient magnitude variable divided by anormalization variable raised to a power that depends on an adjustableparameter; and outputting the normalized gradient magnitude value. Asystem can include a processor; memory operatively coupled to theprocessor; and modules stored in the memory that compriseprocessor-executable instructions to instruct the system to accessseismic data values for a subsurface region that includes a reflector;determine gradient magnitude values based on at least a portion of theseismic data values; normalize each of the gradient magnitude valuesusing a nonlinear normalization equation that includes a gradientmagnitude variable divided by a normalization variable raised to a powerthat depends on an adjustable parameter; and output the normalizedgradient magnitude values. One or more computer-readable storage mediacan include processor-executable instructions to instruct a computingdevice to: access seismic data values for a subsurface region thatincludes a reflector; determine gradient magnitude values based on atleast a portion of the seismic data values; normalize each of thegradient magnitude values using a nonlinear normalization equation thatincludes a gradient magnitude variable divided by a normalizationvariable raised to a power that depends on an adjustable parameter; andoutput the normalized gradient magnitude values. Various otherapparatuses, systems, methods, etc., are also disclosed.

This summary is provided to introduce a selection of concepts that arefurther described below in the detailed description. This summary is notintended to identify key or essential features of the claimed subjectmatter, nor is it intended to be used as an aid in limiting the scope ofthe claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

Features and advantages of the described implementations can be morereadily understood by reference to the following description taken inconjunction with the accompanying drawings.

FIG. 1 illustrates an example system that includes various componentsfor modeling a geologic environment;

FIG. 2 illustrates examples of formations, an example of a conventionfor dip, an example of data acquisition, and an example of a system;

FIG. 3 illustrates examples of normalizations methods;

FIG. 4 illustrates an example of a gradient method:

FIG. 5 illustrates examples of nonlinear normalization methods;

FIG. 6 illustrates examples of methods;

FIG. 7 illustrates examples of data and processed data;

FIG. 8 illustrates examples of data and processed data:

FIG. 9 illustrates examples of data and processed data;

FIG. 10 illustrates examples of processed data;

FIG. 11 illustrates examples of processed data;

FIG. 12 illustrates examples of data and processed data;

FIG. 13 illustrates an example of a method; and

FIG. 14 illustrates example components of a system and a networkedsystem.

DETAILED DESCRIPTION

The following description includes the best mode presently contemplatedfor practicing the described implementations. This description is not tobe taken in a limiting sense, but rather is made merely for the purposeof describing the general principles of the implementations. The scopeof the described implementations should be ascertained with reference tothe issued claims.

In various example embodiments, one or more nonlinear normalizationanalyses may be applied to data such as, for example, seismic data, dataderived from seismic data, other data, etc. As an example, a method mayinclude performing one or more nonlinear normalization analyses todetect features such as, for example, fractures, other latentstructures, etc. As an example, seismic cube nonlinear normalizationanalyses may be implemented in a framework as a module, set of modules,etc., for example, to detect faults, fractures, and latent reflections.As an example, one or more nonlinear normalization analyses may beperformed to assist with detection of one or more features of interestin oil and gas exploration and production (E&P). For example, resultsfrom an analysis may assist with well placement, geologic modeling, sillanalyses, detection of fractured zones or fracture corridors, and in E&Pfor unconventional resources and carbonate fields (e.g., consider shalefields).

Fracture corridors or subtle faults may give rise to seismic signalsthat may be exhibited in acquired seismic data as small-amplitudeself-incoherent features, for example, in cross sections and aslineaments on slices or seismic surfaces. Detection of such features mayinclude processing seismic signals, seismic data or both to generate oneor more edge detection attributes, for example, where an attribute maybe considered a measurable ‘property’ of seismic data (e.g., consideramplitude, dip, frequency, phase, polarity, etc.). For example, anattribute may be a value or a set of values derived from seismicsignals, seismic data, etc. and defined with respect to a coordinatesystem (e.g., one-dimensional, two-dimensional, three-dimensional,four-dimensional or of an even higher dimension). As an example, adimension may be a spatial dimension, a time dimension, a frequencydimension, etc. As an example, consider providing seismic data as a“cube” where each voxel (volume element) in the cube has a value. Insuch an example, an edge detection algorithm may process the values in acube to generate new values where the new values are referred tocollectively as an edge detection attribute (e.g., an attribute cube).

As an example, a seismic cube (e.g., a seismic volume or seismic datafor a volume) may be processed to generate an attribute cube (e.g., anattribute volume or attribute values for a volume). As another example,a seismic surface may be processed to generate an attribute surface. Asyet another example, a seismic line may be processed to generate anattribute line. As an example, a seismic point may be processed togenerate an attribute point.

Attributes may be derived, measured, etc., for example, at one instantin time, for multiple instances in time, over a time window, etc. and,for example, may be measured on a single trace, on a set of traces, on asurface interpreted from seismic data, etc. Attribute analysis mayinclude assessment of various parameters, for example, as to areservoir, consider a hydrocarbon indicator derived from an amplitudevariation with offset (AVO) analysis.

As an example, structures in a subterranean environment may beunderstood better through acquisition of seismic data and processing ofacquired seismic data. Acquired seismic data may exhibit a dynamic rangeof values that may be, for example, about 40 dB between weakest andstrongest reflectors. Such a range of values in a data set (e.g., aseismic image, etc.) can pose issues for edge detection, which may beapplied, for example, to uncover, highlight, etc. structures such asfaults, fractures, etc. Various edge detection algorithms includedetermining gradients (e.g., spatial derivatives of values in a dataset). Where dynamic range is large, gradient values too are likely toexhibit a large dynamic range. As an example, normalization may beapplied in conjunction with edge detection, for example, in an effort toensure that edges in weak reflectors may be as visible as edges instrong reflectors. As an example, a nonlinear normalization techniquemay be applied that includes, for example, an adjustable parameter. Invarious trials, such a nonlinear normalization technique demonstratedstability when applied to seismic data (e.g., raw or processed seismicdata). Output values from application of such a nonlinear normalizationtechnique demonstrated how a selected value of the adjustable parametercan help uncover and highlight structures in a subterranean environment,for example, for a particular purpose. For example, a workflow may aimto determine whether certain structures exist in a subterraneanenvironment and whether those structures exist in some relationship withrespect to other structures. In such an example, the workflow mayimplement a selected value of the adjustable parameter or, optionally,multiple selected values of the adjustable parameter.

As an example, a workflow may include a nonlinear normalization analysiswhere, based on workflow type (e.g., purpose, etc.), a predeterminedparameter value may be specified for the nonlinear normalizationanalysis. As an example, a workflow may include nonlinear normalizationanalyses where, based on workflow type (e.g., purpose, etc.), one ormore predetermined parameter values may be specified for the nonlinearnormalization analyses.

As an example, a nonlinear normalization technique can be applied tooutput a more balanced edge attribute, for example, where edges for bothstrong and weak reflectors are detected simultaneously. Such an approachmay, for example, diminish a number of procedures in a workflow comparedto a workflow where weak and strong reflectors may be processedseparately (e.g., where processing and visualization occur withnormalization turned on and again with normalization turned off).

As an example, application of nonlinear normalization analysis oranalyses to data may help to uncover, highlight, etc. small seismic datafeatures (e.g., small in time, space or both time and space) that may beassociated with faults, fractures, etc. (e.g., small seismic datafeatures associated with seismic energy interacting with faults,fractures, etc.).

As an example, a method may include accessing or providing wellboreinformation, for example, to assist with selection of one or moreadjustable parameter values for a nonlinear normalization technique(e.g., for use in fracture detection, etc.). As an example, fault andfracture auto tracking technology such as ant-tracking may be applied toone or more processed data sets, for example, to improve or enhanceinformation (e.g., consider ant-tracking to generate a fracture image).As an example, detecting may include classifying, for example, whereclassification information (e.g. model information, results frompreviously analyzed data, etc.) may assist in detecting one or morefeatures that may belong to a class of features (e.g., a type offeature).

Below, an example of a system is described followed by varioustechnologies, including examples of techniques, which may, for example,include applying a nonlinear normalization analysis or analyses to data.

FIG. 1 shows an example of a system 100 that includes various managementcomponents 110 to manage various aspects of a geologic environment 150(e.g., an environment that includes a sedimentary basin, a reservoir151, one or more fractures 153, etc.). For example, the managementcomponents 110 may allow for direct or indirect management of sensing,drilling, injecting, extracting, etc., with respect to the geologicenvironment 150. In turn, further information about the geologicenvironment 150 may become available as feedback 160 (e.g., optionallyas input to one or more of the management components 110).

In the example of FIG. 1, the management components 110 include aseismic data component 112, an additional information component 114(e.g., well/logging data), a processing component 116, a simulationcomponent 120, an attribute component 130, an analysis/visualizationcomponent 142 and a workflow component 144. In operation, seismic dataand other information provided per the components 112 and 114 may beinput to the simulation component 120.

In an example embodiment, the simulation component 120 may rely onentities 122. Entities 122 may include earth entities or geologicalobjects such as wells, surfaces, reservoirs, etc. In the system 100, theentities 122 can include virtual representations of actual physicalentities that are reconstructed for purposes of simulation. The entities122 may include entities based on data acquired via sensing,observation, etc. (e.g., the seismic data 112 and other information114). An entity may be characterized by one or more properties (e.g., ageometrical pillar grid entity of an earth model may be characterized bya porosity property). Such properties may represent one or moremeasurements (e.g., acquired data), calculations, etc.

In an example embodiment, the simulation component 120 may rely on asoftware framework such as an object-based framework. In such aframework, entities may include entities based on pre-defined classes tofacilitate modeling and simulation. A commercially available example ofan object-based framework is the MICROSOFT®.NET™ framework (Redmond,Wash.), which provides a set of extensible object classes. In the .NET™framework, an object class encapsulates a module of reusable code andassociated data structures. Object classes can be used to instantiateobject instances for use in by a program, script, etc. For example,borehole classes may define objects for representing boreholes based onwell data.

In the example of FIG. 1, the simulation component 20 may processinformation to conform to one or more attributes specified by theattribute component 130, which may include a library of attributes. Suchprocessing may occur prior to input to the simulation component 120(e.g., consider the processing component 116). As an example, thesimulation component 120 may perform operations on input informationbased on one or more attributes specified by the attribute component130. In an example embodiment, the simulation component 120 mayconstruct one or more models of the geologic environment 150, which maybe relied on to simulate behavior of the geologic environment 150 (e.g.,responsive to one or more acts, whether natural or artificial). In theexample of FIG. 1, the analysis/visualization component 142 may allowfor interaction with a model or model-based results. As an example,output from the simulation component 120 may be input to one or moreother workflows, as indicated by a workflow component 144.

As an example, the simulation component 120 may include one or morefeatures of a simulator such as the ECLIPSE™ reservoir simulator(Schlumberger Limited, Houston Tex.), the INTERSECT™ reservoir simulator(Schlumberger Limited, Houston Tex.), etc. As an example, a reservoir orreservoirs may be simulated with respect to one or more enhancedrecovery techniques (e.g., consider a thermal process such as SAGD,etc.).

In an example embodiment, the management components 110 may includefeatures of a commercially available simulation framework such as thePETREL® seismic to simulation software framework (Schlumberger Limited,Houston, Tex.). The PETREL® framework provides components that allow foroptimization of exploration and development operations. The PETREL®framework includes seismic to simulation software components that canoutput information for use in increasing reservoir performance, forexample, by improving asset team productivity. Through use of such aframework, various professionals (e.g., geophysicists, geologists, andreservoir engineers) can develop collaborative workflows and integrateoperations to streamline processes. Such a framework may be consideredan application and may be considered a data-driven application (e.g.,where data is input for purposes of simulating a geologic environment).

In an example embodiment, various aspects of the management components110 may include add-ons or plug-ins that operate according tospecifications of a framework environment. For example, a commerciallyavailable framework environment marketed as the OCEAN® frameworkenvironment (Schlumberger Limited, Houston, Tex.) allows for integrationof add-ons (or plug-ins) into a PETREL® framework workflow. The OCEAN®framework environment leverages .NET® tools (Microsoft Corporation,Redmond, Wash.) and offers stable, user-friendly interfaces forefficient development. In an example embodiment, various components maybe implemented as add-ons (or plug-ins) that conform to and operateaccording to specifications of a framework environment (e.g., accordingto application programming interface (API) specifications, etc.).

FIG. 1 also shows an example of a framework 170 that includes a modelsimulation layer 180 along with a framework services layer 190, aframework core layer 195 and a modules layer 175. The framework 170 mayinclude the commercially available OCEAN® framework where the modelsimulation layer 180 is the commercially available PETREL® model-centricsoftware package that hosts OCEAN® framework applications. In an exampleembodiment, the PETREL® software may be considered a data-drivenapplication. The PETREL® software can include a framework for modelbuilding and visualization. Such a model may include one or more grids.

The model simulation layer 180 may provide domain objects 182, act as adata source 184, provide for rendering 186 and provide for various userinterfaces 188, Rendering 186 may provide a graphical environment inwhich applications can display their data while the user interfaces 188may provide a common look and feel for application user interfacecomponents.

In the example of FIG. 1, the domain objects 182 can include entityobjects, property objects and optionally other objects. Entity objectsmay be used to geometrically represent wells, surfaces, reservoirs,etc., while property objects may be used to provide property values aswell as data versions and display parameters. For example, an entityobject may represent a well where a property object provides loginformation as well as version information and display information(e.g., to display the well as part of a model).

In the example of FIG. 1, data may be stored in one or more data sources(or data stores, generally physical data storage devices), which may beat the same or different physical sites and accessible via one or morenetworks. The model simulation layer 180 may be configured to modelprojects. As such, a particular project may be stored where storedproject information may include inputs, models, results and cases. Thus,upon completion of a modeling session, a user may store a project. At alater time, the project can be accessed and restored using the modelsimulation layer 180, which can recreate instances of the relevantdomain objects.

In the example of FIG. 1, the geologic environment 150 may includelayers (e.g., stratification) that include a reservoir 151 and that maybe intersected by a fault 153. As an example, the geologic environment150 may be outfitted with any of a variety of sensors, detectors,actuators, etc. For example, equipment 152 may include communicationcircuitry to receive and to transmit information with respect to one ormore networks 155. Such information may include information associatedwith downhole equipment 154, which may be equipment to acquireinformation, to assist with resource recovery, etc. Other equipment 156may be located remote from a well site and include sensing, detecting,emitting or other circuitry. Such equipment may include storage andcommunication circuitry to store and to communicate data, instructions,etc. As an example, one or more satellites may be provided for purposesof communications, data acquisition, etc. For example, FIG. 1 shows asatellite in communication with the network 155 that may be configuredfor communications, noting that the satellite may additionally oralternatively include circuitry for imagery (e.g., spatial, spectral,temporal, radiometric, etc.).

FIG. 1 also shows the geologic environment 150 as optionally includingequipment 157 and 158 associated with a well that includes asubstantially horizontal portion that may intersect with one or morefractures 159. For example, consider a well in a shale formation thatmay include natural fractures, artificial fractures (e.g., hydraulicfractures) or a combination of natural and artificial fractures. As anexample, a well may be drilled for a reservoir that is laterallyextensive. In such an example, lateral variations in properties,stresses, etc. may exist where an assessment of such variations mayassist with planning, operations, etc. to develop the reservoir (e.g.,via fracturing, injecting, extracting, etc.). As an example, theequipment 157 and/or 158 may include components, a system, systems, etc.for fracturing, seismic sensing, analysis of seismic data, assessment ofone or more fractures, etc.

As mentioned, the system 100 may be used to perform one or moreworkflows. A workflow may be a process that includes a number ofworksteps. A workstep may operate on data, for example, to create newdata, to update existing data, etc. As an example, a may operate on oneor more inputs and create one or more results, for example, based on oneor more algorithms. As an example, a system may include a workfloweditor for creation, editing, executing, etc. of a workflow. In such anexample, the workflow editor may provide for selection of one or morepre-defined worksteps, one or more customized worksteps, etc. As anexample, a workflow may be a workflow implementable in the PETREL®software, for example, that operates on seismic data, seismicattribute(s), etc. As an example, a workflow may be a processimplementable in the OCEAN® framework. As an example, a workflow mayinclude one or more worksteps that access a module such as a plug-in(e.g., external executable code, etc.).

FIG. 2 shows an example of a formation 201, an example of a borehole210, an example of a convention 215 for dip, an example of a dataacquisition process 220, and an example of a system 250.

As shown, the formation 201 includes a horizontal surface and varioussubsurface layers. As an example, a borehole may be vertical. As anotherexample, a borehole may be deviated. In the example of FIG. 2, theborehole 210 may be considered a vertical borehole, for example, wherethe z-axis extends downwardly normal to the horizontal surface of theformation 201.

As to the convention 215 for dip, as shown, the three dimensionalorientation of a plane can be defined by its dip and strike. Dip is theangle of slope of a plane from a horizontal plane (e.g., an imaginaryplane) measured in a vertical plane in a specific direction. Dip may bedefined by magnitude (e.g., also known as angle or amount) and azimuth(e.g., also known as direction). As shown in the convention 215 of FIG.2, various angles φ indicate angle of slope downwards, for example, froman imaginary horizontal plane (e.g., flat upper surface); whereas,azimuth refers to the direction towards which a dipping plane slopes(e.g., which may be given with respect to degrees, compass directions,etc.). Another feature shown in the convention of FIG. 2 is strike,which is the orientation of the line created by the intersection of adipping plane and a horizontal plane (e.g., consider the flat uppersurface as being an imaginary horizontal plane).

Some additional terms related to dip and strike may apply to ananalysis, for example, depending on circumstances, orientation ofcollected data, etc. One term is “true dip” (see, e.g., Dip_(T) in theconvention 215 of FIG. 2). True dip is the dip of a plane measureddirectly perpendicular to strike (see, e.g., line directed northwardlyand labeled “strike” and angle α₉₀) and also the maximum possible valueof dip magnitude, Another term is “apparent dip” (see, e.g., Dip_(A) inthe convention 215 of FIG. 2). Apparent dip may be the dip of a plane asmeasured in any other direction except in the direction of true dip(see, e.g., φ_(A) as Dip_(A) for angle α); however, it is possible thatthe apparent dip is equal to the true dip (see, e.g., φ asDip_(A)=Dip_(T) for angle α₉₀ with respect to the strike). In otherwords, where the term apparent dip is used (e.g., in a method, analysis,algorithm, etc.), for a particular dipping plane, a value for “apparentdip” may be equivalent to the true dip of that particular dipping plane.

As shown in the convention 215 of FIG. 2, the dip of a plane as seen ina cross-section perpendicular to the strike is true dip (see, e.g., thesurface with 0 as Dip_(A)=Dip_(T) for angle α₉₀ with respect to thestrike). As indicated, dip observed in a cross-section in any otherdirection is apparent dip (see, e.g., surfaces labeled Dip_(A)).Further, as shown in the convention 215 of FIG. 2, apparent dip may beapproximately 0 degrees (e.g., parallel to a horizontal surface where anedge of a cutting plane runs along a strike direction).

In terms of observing dip in wellbores, true dip is observed in wellsdrilled vertically. In wells drilled in any other orientation (ordeviation), the dips observed are apparent dips (e.g., which arereferred to by some as relative dips). In order to determine true dipvalues for planes observed in such boreholes, as an example, a vectorcomputation (e.g., based on the borehole deviation) may be applied toone or more apparent dip values.

As mentioned, another term that finds use in sedimentologicalinterpretations from borehole images is “relative dip” (e.g., Dip_(R)).A value of true dip measured from borehole images in rocks deposited invery calm environments may be subtracted (e.g., usingvector-subtraction) from dips in a sand body. In such an example, theresulting dips are called relative dips and may find use in interpretingsand body orientation.

A convention such as the convention 215 may be used with respect to ananalysis, an interpretation, an attribute, etc. (see, e.g., variousblocks of the system 100 of FIG. 1). As an example, various types offeatures may be described, in part, by dip (e.g., sedimentary bedding,faults and fractures, cuestas, igneous dikes and sills, metamorphicfoliation, etc.).

Seismic interpretation may aim to identify and classify one or moresubsurface boundaries based at least in part on one or more dipparameters (e.g., angle or magnitude, azimuth, etc.). As an example,various types of features (e.g., sedimentary bedding, faults andfractures, cuestas, igneous dikes and sills, metamorphic foliation,etc.) may be described at least in part by angle, at least in part byazimuth, etc.

As shown in the diagram 220 of FIG. 2, a geobody 225 may be present in ageologic environment. For example, the geobody 225 may be a salt dome. Asalt dome may be a mushroom-shaped or plug-shaped diapir made of saltand may have an overlying cap rock (e.g., or caprock). Salt domes canform as a consequence of the relative buoyancy of salt when buriedbeneath other types of sediment. For example, hydrocarbons may be foundat or near a salt dome due to formation of traps due to salt movement inassociation with evaporite mineral sealing. Buoyancy differentials cancause salt to begin to flow vertically (e.g., as a salt pillow), whichmay cause faulting. In the diagram 220, the geobody 225 is met by layerswhich may each be defined by a dip angle φ.

As an example, seismic data may be acquired for a region in the form oftraces. In the example of FIG. 2, the diagram 220 shows acquisitionequipment 222 emitting energy from a source (e.g., a transmitter) andreceiving reflected energy via one or more sensors (e.g., receivers)strung along an inline direction. As the region includes layers 223 andthe geobody 225, energy emitted by a transmitter of the acquisitionequipment 222 can reflect off the layers 223 and the geobody 225.Evidence of such reflections may be found in the acquired traces. As tothe portion of a trace 226, energy received may be discretized by ananalog-to-digital converter that operates at a sampling rate. Forexample, the acquisition equipment 222 may convert energy signals sensedby sensor Q to digital samples at a rate of one sample per approximately4 ms. Given a speed of sound in a medium or media, a sample rate may beconverted to an approximate distance. For example, the speed of sound inrock may be of the order of around 5 km per second. Thus, a sample timespacing of approximately 4 ms would correspond to a sample “depth”spacing of about 10 meters (e.g., assuming a path length from source toboundary and boundary to sensor). As an example, a trace may be about 4seconds in duration; thus, for a sampling rate of one sample at about 4ms intervals, such a trace would include about 1000 samples where latteracquired samples correspond to deeper reflection boundaries. If the 4second trace duration of the foregoing example is divided by two (e.g.,to account for reflection), for a vertically aligned source and sensor,the deepest boundary depth may be estimated to be about 10 km (e.g.,assuming a speed of sound of about 5 km per second).

In the example of FIG. 2, the system 250 includes one or moreinformation storage devices 252, one or more computers 254, one or morenetworks 260 and one or more modules 270. As to the one or morecomputers 254, each computer may include one or more processors (e.g.,or processing cores) 256 and memory 258 for storing instructions (e.g.modules), for example, executable by at least one of the one or moreprocessors. As an example, a computer may include one or more networkinterfaces (e.g., wired or wireless), one or more graphics cards, adisplay interface (e.g., wired or wireless), etc.

In the example of FIG. 2, the one or more memory storage devices 252 maystore seismic data for a geologic environment that spans kilometers inlength and width and, for example, around 10 km in depth. Seismic datamay be acquired with reference to a surface grid (e.g., defined withrespect to inline and crossline directions). For example, given gridblocks of about 40 meters by about 40 meters, a 40 km by 40 km field mayinclude about one million traces. Such traces may be considered 3Dseismic data where time approximates depth. As an example, a computermay include a network interface for accessing seismic data stored in oneor more of the storage devices 252 via a network. In turn, the computermay process the accessed seismic data via instructions, which may be inthe form of one or more modules.

As an example, one or more attribute modules may be provided forprocessing seismic data. As an example, attributes may includegeometrical attributes (e.g., dip angle, azimuth, continuity, seismictrace, etc.). Such attributes may be part of a structural attributeslibrary (see, e.g., the attribute component 130 of FIG. 1). Structuralattributes may assist with edge detection, local orientation and dip ofseismic reflectors, continuity of seismic events (e.g., parallel toestimated bedding orientation), etc. As an example, an edge may bedefined as a discontinuity in horizontal amplitude continuity withinseismic data and correspond to a fault, a fracture, etc. Geometricalattributes may be spatial attributes and rely on multiple traces.

As mentioned, as an example, seismic data for a region may include onemillion traces where each trace includes one thousand samples for atotal of one billion samples. Resources involved in processing suchseismic data in a timely manner may be relatively considerable bytoday's standards. As an example, a dip scan approach may be applied toseismic data, which involves processing seismic data with respect todiscrete planes (e.g., a volume bounded by discrete planes). Dependingon the size of the seismic data, such an approach may involveconsiderable resources for timely processing. Such an approach may lookat local coherence between traces and their amplitudes, and thereforemay be classified in the category of “apparent dip.”

As an example, imagery such as surface imagery (e.g., satellite,geological, geophysical, etc.) may be processed using a nonlinearnormalization technique. As an example, a method may analyze imageryusing a nonlinear normalization technique to illustrate latentstructure, optionally in conjunction with non-latent structure. As anexample, a framework may access surface imagery and may accesssub-surface seismic data and generate a three-dimensional representation(e.g., for visualization) of surface structure and sub-surfacestructure, which may be joined via an interpolation process or otherprocess. For example, a latent structure may be captured by seismologyand by satellite imagery and a model constructed based at least in parton a nonlinear normalization analysis of seismic data and surfaceimagery.

As an example, ant-tracking may be performed as part of a workflow,which may include, for example, performing nonlinear normalizationanalysis on data and then generating ant track data, from which, forexample, features may be extracted (e.g., patches). In turn, suchfeatures may be subject to one or more of validation, editing or otherprocess. Ant-tracking may generate an ant-tracking attribute, anant-tracking surface, an ant-tracking volume (e.g., or cube), etc.

Ant-tracking may include using an algorithm that by analogy, involves“ants” finding the shortest path between their nest and their foodsource (e.g., by communicating using pheromones to attract other ants).In such an example, the shortest path becomes marked with morepheromones than longer paths such that subsequent ants are more likelyto choose the shortest path, and so on.

Where features may be latent (e.g., latent structure), for example, dueto noise, acquisition footprint, etc., performing nonlinearnormalization analysis prior to ant-tracking may enhance the ability totrack the latent features, particularly where the features have someamount of continuity (e.g., contiguous within a surface, a volume,etc.). For example, fractures generated by a fracturing process (e.g.,consider hydraulic fracturing) can tend to be relatively small (e.g.,compared to faults) and contiguous.

FIG. 3 shows an example of a method 310 that includes an input block 314for inputting values, a normalization block 318 for normalizing valuesand an output block 322 for outputting normalized values. FIG. 3 alsoshows an example of an equation 330 for a linear normalization and anequation 350 for a dual parameter denominator sigmoid normalizationtechnique.

As to the linear equation 330, for example, if a range of values of adata set is from 50 to 180 and a desired range is 0 to 255, a linearnormalization technique can include subtracting 50 from each of thevalues, making the range from 0 to 130 followed by multiplication by255/130 to make the range from 0 to 255.

As to the equation 350 of the sigmoid technique, it can focus on aparticular range of values and progressively attenuate values outsidethat range. In the equation 350, I is the input value, I_(N) is theoutput value, ΔI_(new) is the difference between the new minimum (alsoI−nmin) and maximum values, α defines the width of the input valuerange, and β defines the value around which the range is centered.

FIG. 4 shows an example of data 410, an example of a gradient kernel420, an example of a gradient kernel 430 and an example, of a gradientequation 440. As an example, a kernel may be implemented to convolvedata Kernels find use in various types of filters. For example, considerthe Sobel filter (e.g., also referred to as the Sobel operator), whichincludes so-called Sobel kernels. The Sobel filter finds use in edgedetection. The Sobel filter is a discrete differentiation operator thatcan estimate the gradient of an image intensity function, for example,where at each point in the image, the result of the Sobel filter iseither the corresponding gradient vector or the norm of this vector. TheSobel filter may be implemented by convolving an image with a small,separable, and integer valued operator (e.g., of several pixels in size)in horizontal and vertical directions.

As an example, the Sobel filter may include two 3×3 kernels which areconvolved with an original image to estimate horizontal and verticalderivatives, which may be output as derivative images Gx and Gy. As anexample, a gradient value (e.g., gradient magnitude) or gradient imagemay be generated based on Gx and Gy, for example, as indicated by theequation 440.

In FIG. 4, the gradient kernel 420 is shown as being a −1, 0, 1 kernelfor an x-direction and the gradient kernel 430 is shown as being a −1,0, 1 kernel for a y-direction. Examples of data values (e.g., intensityvalues) are shown, for example, with reference to the data 410 wherevalues range from 0 to 255.

FIG. 5 shows an example of data 510, an example of a gradient magnitudeequation 520, an example of a nonlinear normalization equation 530, anexample of a unidirectional gradient nonlinear normalization equation540, an example of another unidirectional gradient nonlinearnormalization equation 550 and an example of a method 580.

In FIG. 5, the nonlinear normalization equations 530, 540 and 550include a term in the denominator (e.g., a nonlinear normalization term)that includes a parameter k. In particular, the nonlinear normalizationterms, in two-dimensions, may be represented by the following equation:I(x,y)^((1/k)).

In FIG. 5, the nonlinear normalization equations 530, 540 and 550 may bereferred to as mixed gradient and intensity equations. For example, eachof the equations includes at least one gradient value and at least oneintensity value. While intensity is mentioned, it may be raw data,processed data, etc. For example, the nomenclature “I” may refer to anattribute value whereas the nomenclature G″ refers to a gradient basedon a plurality of attribute values.

As to the parameter k, where k is equal to 1, the nonlinearnormalization equation can normalize gradient values linearly withrespect to I (e.g., I^((1/1))=I); whereas, as k increases, for example,to 10, the nonlinear normalization diminishes (e.g., reaching a limit ofno normalization because as the value of k increases, the term 1/kapproaches zero; for example, I(x,y) raised to a power of about 0 wouldbe approximately unity). As an example, where k=2, the nonlinearnormalization may be referred to as square normalization and where k=3,the nonlinear normalization may be referred to as cubic normalization.As an example, as k approaches zero, the output may be compressed (e.g.,to values less than the un-normalized gradient magnitude G). As anexample, k may be a number, which may be an integer or a real number. Asan example, k may be a number different than unity. As an example, k maybe a number less than one. As an example, k may be a number greater thanone. As an example, k may be a number less than one or k may be a numbergreater than one.

As shown in FIG. 5, the method 580 includes a provision block 582 forproviding data values for a region: a determination block 584 fordetermining a gradient magnitude value based on at least a portion ofthe data values; a normalization block 586 for normalizing the gradientmagnitude value using a nonlinear normalization equation that includes agradient magnitude variable divided by a normalization variable raisedto a power that depends on an adjustable parameter; and an output block588 for outputting the normalized gradient magnitude value. In such anexample, the data values for a region may be or include seismic datavalues for a subsurface region. In such an example, the subsurfaceregion may include a reflector. For example, the seismic data values maybe values based at least in part on energy reflected from a reflector(e.g., a subsurface structure, etc.).

As an example, the method 580 may include providing data values for aregion where the data values are or include imagery data values. As anexample, imagery data values may be X-ray, NMR, microwave, etc. or otherimagery data values. As an example, data values may be acquired viasatellite equipment. As an example, satellite equipment may beconfigured to acquire data in one or more a visible, a panchromatic, amid-infrared, a thermal infrared or other region of an electromagneticspectrum (e.g., consider LANDSAT data and/or other types of satellitedata). As an example, data values may include information as to climate(e.g., temperature, wind, water currents, clouds, rain, snow, ice,etc.). As an example, data values may be acquired using one or moreremote-sensing technologies (e.g., radar, etc.). As an example, datavalues may be or include data values acquired via a sensor array orsensor arrays (e.g., as in a camera, X-ray detector, etc.). As anexample, a method may include detecting one or more edges in imagerydata.

The method 580 of FIG. 5 is shown as being associated with variouscomputer-readable media (CRM) blocks 583, 585, 587 and 589. Such blocksgenerally include instructions suitable for execution by one or moreprocessors (or processor cores) to instruct a computing device or systemto perform one or more actions. As an example, a single medium may beconfigured with instructions to allow for, at least in part, performanceof various actions of one or more of the method 580. As an example, acomputer-readable medium (CRM) may be a computer-readable storage medium(e.g., a non-transitory medium).

FIG. 6 shows an example of data 601 and examples of methods 610, 620,630 and 640. The data 601 show data values where a strong gradientexists which may be an edge that traverses the spatial region (e.g.diagonally). As an example, an edge may traverse a spatial region in aparticular direction while, for example, another edge may traverse thespatial region in a different direction or the same direction. In themethods 610, 620, 630 and 640, input blocks 612, 622, 632 and 642, maybe selected based on information or may be selected to help uncover orhighlight information, for example, as to directions of one or moreedges, which may correspond to structure or structures (e.g., in asubterranean environment, an upper surface of the Earth, etc.).

As shown in FIG. 6, each of the methods 610, 620, 630 and 640 includesan input block 612, 622, 632 and 642 for inputting values, anormalization block 614, 624, 634 and 644 for normalizing values (e.g.,using a nonlinear normalization equation that includes an adjustableparameter), and an output block 616, 626, 636 and 646 for outputtingnormalized values. As an example, input values may be gradient valuesand a nonlinear normalization equation may include a non-gradient valueas a variable such that gradient values are normalized by non-gradientvalues (e.g., intensity values).

As an example, a gradient value or gradient values may be determinedusing a kernel or kernels that center on a spatial location, forexample, where a value for that spatial location is used for normalizinga gradient value or for normalizing gradient values. For example, theequations 530, 540 and 550 of FIG. 5 can nonlinearly normalize agradient value based on a non-gradient value (e.g., an intensity valuefor a spatial location).

The methods 610, 620, 630 and 640 in FIG. 6 may be associated withvarious computer-readable media (CRM) blocks. Such blocks generallyinclude instructions suitable for execution by one or more processors(or processor cores) to instruct a computing device or system to performone or more actions. As an example, a single medium may be configuredwith instructions to allow for, at least in part, performance of variousactions of one or more of the methods 610, 620, 630 and 640. As anexample, a computer-readable medium (CRM) may be a computer-readablestorage medium (e.g., a non-transitory medium).

As an example, a method can include providing data values for a region;determining gradient values for at least a portion of the data values;normalizing the gradient values using a nonlinear normalization equationthat comprises a seismic data value variable and an adjustable parameterterm; and outputting normalized output data values. As an example, amethod that includes providing data values may include accessing memory,a storage device, etc., for example, that stores such data values. Forexample, a processor may execute instructions that cause the processorto access data values.

As an example, output data values may optionally be enhanced via one ormore processes (e.g., image processing, ant-tracking, etc.). As anexample, a method can include performing ant-tracking on at least aportion of normalized output data values. As an example, a method caninclude outputting ant-tracking data values based at least in part onperforming ant-tracking.

As an example, a region may include a subsurface region, which mayinclude, for example, shale. As an example, a subsurface region mayinclude or be a layer and, for example, include at least a portion of areflector (e.g., a reflector that intersects the layer). As an example,a reflector may be a reflector of a fracture, for example, where thefracture may have been generated by a hydraulic fracturing process(e.g., optionally using proppant). As an example, a method may includeperforming a fracturing process on a subsurface region based at least inpart on output data values from a nonlinear normalization analysis. Asan example, a subsurface region may include multiple reflectorsassociated with artificial fractures in the subsurface region.

As an example, a method can include providing data values for a region;determining a gradient magnitude value based on at least a portion ofthe data values; normalizing the gradient magnitude value using anonlinear normalization equation that includes a gradient magnitudevariable divided by a normalization variable raised to a power thatdepends on an adjustable parameter; and outputting the normalizedgradient magnitude value. In such an example, the power may be greaterthan or equal to 2.

As an example, a normalization variable may be one of a set of datavalues used for determining a gradient magnitude value. As an example, anormalization variable may be a largest magnitude data value of datavalues used for determining a gradient magnitude value.

As an example, an adjustable parameter may be denoted k and where apower may be k⁻¹. As an example, a variable may be “I” and a parameter“k” and an exponentiation I^((1/k)) (e.g., the variable I raised to apower that depends on the reciprocal of the parameter k).

As an example, a subsurface region (e.g., a subterranean environment)may include reflectors. As an example, such reflectors may includedifferent classes of reflectors. For example, depending on reflectionproperties, a reflector may be classified as to how much energy itreflects, for example, along a spectrum from weak to strong.

As an example, a reflector may be an interface between layers ofcontrasting acoustic, optical or electromagnetic properties. In such anexample, waves of electromagnetism, heat, light and sound may bereflected at such an interface. As an example, as to seismic data, areflector might represent a change in lithology, a fault, anunconformity, etc. As an example, a reflector may be expressed as areflection (e.g., or reflections) in seismic data.

As an example, an adjustable parameter may be selected based on at leastin part on a class or classes of reflectors. As an example, reflectorsmay include a strong class of reflectors and a weak class of reflectors.

As an example, a method may be performed, at least in part, using acomputing device, a system that includes one or more processors, etc.

As an example, a method may include repeating a determining process, anormalizing process and an outputting process, for example, to generatea multi-dimensional set of normalized gradient values. In such anexample, a method may include performing ant-tracking on themulti-dimensional set of normalized gradient values.

As an example, a method may include determining a set of gradientmagnitude values, applying a mean filter to the set of gradientmagnitude values to generate a filtered gradient magnitude value andnormalizing the filtered gradient magnitude value using a nonlinearnormalization equation.

As an example, a system can include a processor; memory operativelycoupled to the processor; and modules stored in the memory that compriseprocessor-executable instructions to instruct the system to access datavalues for a region; determine gradient magnitude values based on atleast a portion of the data values; normalize each of the gradientmagnitude values using a nonlinear normalization equation that includesa gradient magnitude variable divided by a normalization variable raisedto a power that depends on an adjustable parameter; and output thenormalized gradient magnitude values.

As an example, a system can include a processor; memory operativelycoupled to the processor; and modules stored in the memory that compriseprocessor-executable instructions to instruct the system to accessseismic data values for a subsurface region that includes a reflector;determine gradient magnitude values based on at least a portion of theseismic data values; normalize each of the gradient magnitude valuesusing a nonlinear normalization equation that includes a gradientmagnitude variable divided by a normalization variable raised to a powerthat depends on an adjustable parameter; and output the normalizedgradient magnitude values. In such an example, the normalizationvariable may be one of the seismic data values used for determining acorresponding one of the gradient magnitude values. As an example, anormalization variable may be a largest magnitude seismic data value ofseismic data values used for determining a corresponding gradientmagnitude value.

As an example, a system may include a module or modules that includeprocessor-executable instructions to instruct the system to apply a meanfilter to gradient magnitude values to generate filtered gradientmagnitude values and to normalize the filtered gradient magnitude valuesusing a nonlinear normalization equation.

As an example, one or more computer-readable storage media can includeprocessor-executable instructions to instruct a computing device to:access data values for a region; determine gradient magnitude valuesbased on at least a portion of the data values; normalize each of thegradient magnitude values using a nonlinear normalization equation thatincludes a gradient magnitude variable divided by a normalizationvariable raised to a power that depends on an adjustable parameter; andoutput the normalized gradient magnitude values

As an example, one or more computer-readable storage media can includeprocessor-executable instructions to instruct a computing device to:access seismic data values for a subsurface region that includes areflector; determine gradient magnitude values based on at least aportion of the seismic data values; normalize each of the gradientmagnitude values using a nonlinear normalization equation that includesa gradient magnitude variable divided by a normalization variable raisedto a power that depends on an adjustable parameter; and output thenormalized gradient magnitude values. In such an example, thenormalization variable may be one of the seismic data values used fordetermining a corresponding one of the gradient magnitude values. As anexample, a normalization variable may be a largest magnitude seismicdata value of seismic data values used for determining a correspondinggradient magnitude value.

FIG. 7 shows input data values 710 and processed data values 720, 730and 740. The input data values 710 include seismic inline data values,the processed data values 720 include lateral derivative values in theinline direction without normalization (e.g., k large), the processeddata values 730 include lateral derivative values in the inlinedirection normalized (e.g., k=1) and the processed data values 740include lateral derivative values in the inline direction normalized(e.g., k=2).

As an example, low-amplitude reflectors may have low-amplitude gradients(e.g., a relatively large change in a small value range remains a smallvalue). Referring to the processed data values 720, where lateralchanges in the strong reflector near center (e.g., location indicated bythe cross-hairs) stand out, however, a fault to the far left (e.g.,surrounded by weak reflectors) is less visible. As an example, to makeedge-detection insensitive to reflector amplitude, a method can includenormalizing the spatial derivatives by the magnitude of the seismicamplitudes.

Referring to the processed data values 730, the lateral derivativevalues in the inline direction are normalized by the magnitude (i.e.,absolute value) of the lateral derivative with the factor |I|^(1/1)=|I|,where I is the amplitude of the seismic data value at the location forwhich the derivative is determined (e.g., a centered spatial location).As seen in FIG. 7, the fault to the left in the image is highlighted,while lateral changes for the strong reflector are more muted.

As an example, a method may include highlighting lateral changes forboth the strong and the weak reflectors simultaneously in the seismicdata values 710. Referring to the processed data values 740,normalization may use, as an example, the term |I|^(½)=Sqrt(|I|), whichprovides for highlighting changes in both the strong and the weakreflectors.

As an example, a method can include normalizing a gradient value (e.g.,gradient magnitude, etc.) using a term I^(1/k). For example, a methodcan include normalizing the lateral derivative of seismic data valueswith the term |I|^(1/k), where k is a number (e.g., integer, fraction,rational, complex, etc.) and where |I| is a positive value, for example,representative of a value range of numbers used to calculate aderivative or derivatives (e.g. center value, mean value, max value,etc.).

FIG. 8 shows input data values 810 and processed data values 820, 830and 840. The input data values 810 correspond to those of 710, however,the processed data values 820, 830 and 840 are values generated, forexample, by application of a 3×9 sample mean filter to the normalizedlateral derivatives. As demonstrated in FIG. 8, such a mean filter canenhance vertical continuity of the fault planes and attenuate randomnoise. Referring to the processed data values 840, (normalization factork=2, i.e. Sqrt(|I|)), a balanced “blend” is generated, for example, ofthe results per the processed data values 820 and 830, which, in thisexample, yields a better overall continuity of the fault planes. In theexamples of FIG. 7 and FIG. 8, the color scales include differentminimum and maximum values, for example, due to use of differentnormalization terms.

FIGS. 9, 10 and 11 show example seismic data values 919 and exampleprocessed seismic data values 920, 930, 940, 950 and 960. The seismicdata values 910 correspond to a horizontal time-slice through arepresentative 3D seismic cube (e.g., a seismic data volume). The datavalues were processed to provide magnitude of the spatial derivatives inthe x and y directions and to then determine the RMS sum of thosederivatives, which yielded the processed seismic data values 920.

Referring to the processed seismic data values 920, strong reflectorscan more strongly indicate the presence of a fault than the weakreflectors. As an example, a method may include correcting forunder-estimation of edges for weak reflectors. In such a method,normalization may be applied. For example, a normalization factor mayaim to represent the strength of the reflectors, for example, usingamplitude magnitude for center pixel used in the gradient calculation,amplitude magnitude for strongest pixel used in the gradientcalculation, mean amplitude magnitude for considered pixel values, RMSor mean amplitude magnitude in a spatial window in the proximity of thecenter pixel. Of these examples, amplitude magnitude finds use, forexample, in determination of coherency, variance and amplitude contrastseismic attributes. However, when such an approach is applied, for veryweak reflectors, it can be unstable because of risk of division-by-zero.

As an example, a method may include normalizing based at leas in part onan amplitude magnitude for a strongest pixel used in a gradientdetermination. For example, referring to FIG. 4, the maximum amplitudemagnitude for the gradient determinations 420 and 430 is “255” (e.g.,greatest intensity of the three in an x-direction kernel or ay-direction kernel).

Referring to FIG. 10 and processed seismic data values 930, these resultfrom normalization using the strongest seismic data value within agradient determination kernel (e.g., or kernels). As shown, edges forweak reflectors are now more visible and, overall, the result appearsintuitively better than for the processed seismic data values 920 ofFIG. 9.

As an example, consider that a normalization proportional to theamplitude/energy level of the reflectors at hand may tend tounder-estimate the presence of discontinuities for strong reflectors.Such a tendency finds support in practical and empirical aspects as tothe nature of seismic reflections. Accordingly, as an example, dependingon what may be desired from an investigation (e.g., a workflow), asmaller norm may be appropriate for strong reflectors than for weakreflectors.

As an example, consider seismic waves propagating through theunderground to be measured by pressure sensors or accelerometers. Bothof these types of sensors measure the energy level of the propagatingacoustic and elastic wave propagation, not the magnitude (amplitude) ofthe waves. Amplitude may be considered to be proportional to the squareroot of energy and energy of seismic signals can tend to be proportionalto the square of amplitude. Given such considerations, a normproportional to the square root of recorded energy level may beappropriate, for example, depending on desired outcome (e.g., of aworkflow, etc.). Thus, as an example, a strong reflector may have acomparatively smaller norm than a weak reflector where the square rootof the norm is used to scale gradient values.

As an example, for a two-dimensional scenario, the following equationmay be provided:Result(x,y)=Sqrt(dIx*dIx+dIy*dIy)/norm^(1/k)

in the foregoing example equation, dIx may represent the spatialderivative of an image (e.g., for the point (x,y)) in the x direction,and dIy may represent the spatial derivative of the image (e.g., for thepoint (x,y)) in the y direction. As an example, the norm value may be arepresentative value of the strength of the data, the pixel, the voxel,the sample, etc. and k may be a predefined value or, for example, auser-defined value (e.g., optionally a constant). As an example, amethod may include a default setting for the parameter k, for example, kis set to be equal to 2 (e.g., to take the square root of the norm asthe actual norm used).

In FIG. 10, the processed data values 940 correspond to k=2 and acomparison can be made to the processed data values 930, where nomodified norm is used. From such a comparison, strong reflectors canhave a much more pronounced edge indication, while the more noisy partsof the weak reflectors have been somewhat muted, which may be deemed adesirable result.

As an example, a seismic edge detection process may include computingspatial derivatives along an estimated local 3D layering (e.g., dipcorrection) and also adding an element of vertical smoothing, forexample, to help ensure continuity in the vertical (depth domain)dimension. Where these two techniques are applied (e.g., dip correctionand vertical smoothing), one arrives at the processed seismic data 950of FIG. 11. The processed seismic data 950 of FIG. 11 corresponds to theprocessed seismic data 940 of FIG. 10 with dip-corrected spatialderivatives and vertical smoothing (e.g., with a radius of two samplesin the vertical dimension).

In FIG. 11, the processed seismic data 960 corresponds to a comparablehorizontal section (time-slice) result using the variance seismicattribute (parameters=3, 3, 7) in the PETREL® framework.

FIG. 12 shows examples of input seismic data 1210 and processed seismicdata 1220, 1230, 1240, 1250 and 1260. The input seismic data 1210corresponds to an inline seismic section.

The processed seismic data 1220 corresponds to normalized edge result,using k=1. As seen in FIG. 12, many small discontinuities existthroughout the processed seismic data 1220; noting that for k=1, resultsare sensitive to low-amplitude noise.

The processed seismic data 1230 correspond to a normalized edge result,using k=1.5: and the processed seismic data 1240 corresponds to anormalized edge result, using k=2 (i.e. squared normalization).

The processed seismic data 1250 correspond to a normalized edge result,using k=99 (e.g., normalization approximately unity), which means thatpractically no amplitude correction is applied. As such, strongreflectors can dominate the result; noting that some faults arewell-mapped for this k value.

The processed seismic data 1260 correspond to a comparable verticalsection (inline) result using the variance seismic attribute(parameters=3, 3, 7) in the PETREL® framework.

FIG. 13 shows an example of a method 1310 that includes an access block1312 for accessing one or more databases (e.g., data stores, datastorage devices, etc.), an input block 1314 for inputting values (e.g.,optionally accessed via the access block 1312), an input block 1316 forinputting parameter (k) values (e.g., optionally accessed via the accessblock 1312) a normalization block 1318 for normalizing input valuesbased at least in part on the parameter (k) values, an output block 1322for outputting normalized values for each of the various parameter (k)values (see, e.g., outputs 1324-1 to 1324-N), an output and/or controlblock 1326 for outputting information to and/or controlling a system. Asshown in the example of FIG. 13, the method 1310 may include aclassification block 1330, for example, to classify parameter (k)values, optionally for purposes of storing the parameter (k) values withrespect to a class in a database, providing particular parameter (k)values as input and/or outputting and/or controlling a system based atleast in part on a classification or classifications. For example, wherea parameter (k) value is classified as to fractures that may beartificial fractures, information may be output to a field site or otheroperational site for purposes of further fracturing (e.g., injecting,etc.).

As an example, the method 1310 may include a process block forprocessing such as ant-tracking. For example, where particularstructural features are highlighted in output results using one or moreof the parameter (k) values, ant-tracking may be applied to thoseresults. Such an approach may facilitate determination of locations ofstructures that may be indicated by weak reflectors, strong reflectorsor weak and strong reflectors in seismic data values (e.g., from aseismic study of a subterranean environment).

As an example, a Radon transform may be applied by a process block, forexample, for purposes of line extraction (e.g., edge detection). As anexample, a method may include detecting faults that stem from oldearthquakes, oil accumulations, artificial fracturing (e.g., hydraulicfracturing), etc. As an example, a method may include detecting featuresand mapping such feature prior to drilling, for example, to avoiddrilling through active faults. For example, a method such as the method1310 may be implemented to uncover and/or highlight active faults.

As an example, a method may include tracking changes in a subterraneanenvironment with respect to time. For example, changes may be due toartificial fractures, sedimentation as to depletion of a reservoir, etc.Such a method may include assessing different generations of seismicdata, one data, another data set, etc. and examining processed data fordifferences (e.g. as an indication of a response to pressure,production, injection, etc.).

As an example, a system may include one or more modules, which may beprovided to analyze data, control a process, perform a task, perform aworkstep, perform a workflow, etc.

FIG. 14 shows components of an example of a computing system 1400 and anexample of a networked system 1410. The system 1400 includes one or moreprocessors 1402, memory and/or storage components 1404, one or moreinput and/or output devices 1406 and a bus 1408. In an exampleembodiment, instructions may be stored in one or more computer-readablemedia (e.g., memory/storage components 1404). Such instructions may beread by one or more processors (e.g., the processor(s) 1402) via acommunication bus (e.g., the bus 1408), which may be wired or wireless.The one or more processors may execute such instructions to implement(wholly or in part) one or more attributes (e.g., as part of a method).A user may view output from and interact with a process via an I/Odevice (e.g., the device 1406). In an example embodiment, acomputer-readable medium may be a storage component such as a physicalmemory storage device, for example, a chip, a chip on a package, amemory card, etc. (e.g., a computer-readable storage medium).

In an example embodiment, components may be distributed, such as in thenetwork system 1410. The network system 1410 includes components 1422-1,1422-2, 1422-3, . . . 1422-N. For example, the components 1422-1 mayinclude the processor(s) 1402 while the component(s) 1422-3 may includememory accessible by the processor(s) 1402. Further, the component(s)1402-2 may include an I/O device for display and optionally interactionwith a method. The network may be or include the Internet, an intranet,a cellular network, a satellite network, etc.

As an example, a device may be a mobile device that includes one or morenetwork interfaces for communication of information. For example, amobile device may include a wireless network interface (e.g., operablevia IEEE 802.11, ETSI GSM, BLUETOOTH®, satellite, etc.). As an example,a mobile device may include components such as a main processor, memory,a display, display graphics circuitry (e.g., optionally including touchand gesture circuitry), a slot, audio/video circuitry, motion processingcircuitry (e.g., accelerometer, gyroscope), wireless LAN circuitry,smart card circuitry, transmitter circuitry, GPS circuitry, and abattery. As an example, a mobile device may be configured as a cellphone, a tablet, etc. As an example, a method may be implemented (e.g.,wholly or in part) using a mobile device. As an example, a system mayinclude one or more mobile devices.

As an example, a system may be a distributed environment, for example, aso-called “cloud” environment where various devices, components, etc.interact for purposes of data storage, communications, computing, etc.As an example, a device or a system may include one or more componentsfor communication of information via one or more of the Internet (e.g.,where communication occurs via one or more Internet protocols), acellular network, a satellite network, etc. As an example, a method maybe implemented in a distributed environment (e.g., wholly or in part asa cloud-based service).

As an example, information may be input from a display (e.g., consider atouchscreen), output to a display or both. As an example, informationmay be output to a projector, a laser device, a printer, etc. such thatthe information may be viewed. As an example, information may be outputstereographically or holographically. As to a printer, consider a 2D ora 3D printer. As an example, a 3D printer may include one or moresubstances that can be output to construct a 3D object. For example,data may be provided to a 3D printer to construct a 3D representation ofa subterranean formation. As an example, layers may be constructed in 3D(e.g., horizons, etc.), geobodies constructed in 3D, etc. As an example,holes, fractures, etc., may be constructed in 3D (e.g., as positivestructures, as negative structures, etc.).

Although only a few example embodiments have been described in detailabove, those skilled in the art will readily appreciate that manymodifications are possible in the example embodiments. Accordingly, allsuch modifications are intended to be included within the scope of thisdisclosure as defined in the following claims. In the claims,means-plus-function clauses are intended to cover the structuresdescribed herein as performing the recited function and not onlystructural equivalents, but also equivalent structures. Thus, although anail and a screw may not be structural equivalents in that a nailemploys a cylindrical surface to secure wooden parts together, whereas ascrew employs a helical surface, in the environment of fastening woodenparts, a nail and a screw may be equivalent structures. It is theexpress intention of the applicant not to invoke 35 U.S.C. §112,paragraph 6 for any limitations of any of the claims herein, except forthose in which the claim expressly uses the words “means for” togetherwith an associated function.

What is claimed is:
 1. A method comprising: receiving imagery datavalues for a region; determining a gradient magnitude value based on atleast a portion of the data values; normalizing the gradient magnitudevalue using a nonlinear normalization equation that comprises a gradientmagnitude variable divided by a normalization variable raised to a powerthat depends on an adjustable parameter k wherein the power is k⁻¹ andwherein k is a rational number greater than one; and outputting to adisplay an edge enhanced image that is based at least in part on thenormalized gradient magnitude value.
 2. The method of claim 1 whereinthe data values for a region comprise seismic data values for asubsurface region that comprises a reflector.
 3. The method of claim 1wherein the power is a number greater than or equal to approximately 2.4. The method of claim 1 wherein the normalization variable comprisesone of the data values used for determining the gradient magnitudevalue.
 5. The method of claim 1 wherein the normalization variablecomprises the largest magnitude data value of the data values used fordetermining the gradient magnitude value.
 6. The method of claim 2wherein the subsurface region comprises reflectors wherein thereflectors comprise different classes of reflectors.
 7. The method ofclaim 6 wherein the adjustable parameter is selected based at least inpart on one of the different classes of reflectors.
 8. The method ofclaim 1 further comprising performing the normalizing using a computingdevice.
 9. The method of claim 1 further comprising repeating thedetermining, the normalizing and the outputting to generate amulti-dimensional set of normalized gradient values.
 10. The method ofclaim 9 further comprising performing ant-tracking on themulti-dimensional set of normalized gradient values.
 11. The method ofclaim 1 further comprising determining a set of gradient magnitudevalues, applying a mean filter to the set of gradient magnitude valuesto generate a filtered gradient magnitude value and normalizing thefiltered gradient magnitude value using the nonlinear normalizationequation.
 12. A system comprising: a processor; memory operativelycoupled to the processor; modules stored in the memory that compriseprocessor-executable instructions to instruct the system to accessimagery data values for a region; determine gradient magnitude valuesbased on at least a portion of the data values; normalize each of thegradient magnitude values using a nonlinear normalization equation thatcomprises a gradient magnitude variable divided by a normalizationvariable raised to a power that depends on an adjustable parameter kwherein the power is k⁻¹ and wherein k is a rational number greater thanone; and output to a display an edge enhanced image that is based atleast in part on the normalized gradient magnitude values.
 13. Thesystem of claim 12 wherein the imagery data values comprise seismic datavalues and wherein the normalization variable comprises one of theseismic data values used for determining a corresponding one of thegradient magnitude values.
 14. The system of claim 12 wherein theimagery data values comprise seismic data values and wherein thenormalization variable comprises the largest magnitude seismic datavalue of the seismic data values used for determining a correspondinggradient magnitude value.
 15. The system of claim 12 wherein the modulescomprise processor-executable instructions to instruct the system toapply a mean filter to the gradient magnitude values to generatefiltered gradient magnitude values and to normalize the filteredgradient magnitude values using the nonlinear normalization equation.16. One or more computer-readable storage media comprisingprocessor-executable instructions to instruct a computing device to:access imagery data values for a region; determine gradient magnitudevalues based on at least a portion of the seismic data values; normalizeeach of the gradient magnitude values using a nonlinear normalizationequation that comprises a gradient magnitude variable divided by anormalization variable raised to a power that depends on an adjustableparameter k wherein the power is k⁻¹ and wherein k is a rational numbergreater than one; and output to a display an edge enhanced image that isbased at least in part on the normalized gradient magnitude values. 17.The one or more computer-readable storage media of claim 16 wherein theimagery data values comprise seismic data values and wherein thenormalization variable comprises one of the seismic data values used fordetermining a corresponding one of the gradient magnitude values. 18.The one or more computer-readable storage media of claim 16 wherein theimagery data values comprise seismic data values and wherein thenormalization variable comprises the largest magnitude seismic datavalue of the seismic data values used for determining a correspondinggradient magnitude value.